Our data set consists of MRI scans of \(N=1010\)  unique subjects from the Human Connectome Project (HCP) Q4 release [1]}, aged 22-36 years (mean 29 years), acquired from a diverse population including 468 males and 542 females, and 434 unique families. The MRI data are provided as T1w volumes at isotropic 0.7mm voxel resolution and preprocessed via a standard neuroimaging pipeline, including rigid subject-to-atlas registration and skull-stripping. Keypoint extraction requires approximately 3 sec. / per image and results in an average of 1,400 keypoints per image for a total of 1,488,065 keypoints. We use a GPU implementation [2]} of the 3D SIFT-Rank keypoint algorithm [3]} that produces identical keypoints at a 7\(\times \)  speedup. Approximate kNN correspondences between appearance descriptors are identified across the entire database using efficient KD-tree indexing [4]}, where lookup requires 0.8 sec. / subject for k=200 nearest neighbors on an i7-5600@2.60Ghz machine with 16 GB RAM (1.64 GB used).
